Understanding the spatial context in which molecular interactions take place is becoming increasingly important in the study of biological and pathological processes in living organisms. The spatial distribution of biomolecules and the localization of biochemical interactions throughout tissue often hold crucial clues toward determining the biological functions of these biomolecules. Imaging mass spectrometry, IMS (R. M. Caprioli, T. B. Farmer, and J. Gile, Anal. Chem. 1997, 69:4751-4760, and L. A. McDonnell, and R. M. A. Heeren, Mass Spectrom. Rev. 2007, 26:606-643) is a molecular imaging technology that can deliver such spatial information with high chemical specificity for various classes of biomolecules, including metabolites, lipids, peptides, and proteins.
IMS has been gaining considerable momentum in recent years, primarily in the field of tissue biomarkers (K. Chughtai and R. M. A. Heeren, Chem. Rev. 2010, 110:3237-3277, and A. Walch, S. Rauser, S.-O. Deininger, and H. Höfler, Histochem. Cell Biol. 2008, 130:421-434) and drug delivery (N. Takai, Y. Tanaka, K. Inazawa, and H. Saji, Rapid Commun. Mass Spectrom. 2012, 26:1549-1556, and P. J. Trim, C. M. Henson, J. L. Avery, A. McEwen, M. F. Snel, E. Claude, P. S. Marshall, A. West, A. P. Princivalle, and M. R. Clench, Anal. Chem. 2008, 80:8628-8634), and has been successfully applied to tissues of various origin, including insect (Y. Sugiura, Y. Konishi, N. Zaima, S. Kajihara, H. Nakanishi, R. Taguchi, and M. Setou, J. Lipid Res. 2009, 50:1776-1788), mammalian (M. Stoeckli, P. Chaurand, D. E. Hallahan, and R. M. Caprioli, Nat. Med. 2001, 7:493-496), and human tissue (S. Khatib-Shahidi, M. Andersson, J. L. Herman, T. A. Gillespie, and R. M. Caprioli, Anal. Chem. 2006, 78:6448-6456; L. S. Eberlin, I. Norton, A. L. Dill, A. J. Golby, K. L. Ligon, S. Santagata, R. G. Cooks, and N. Y. R. Agar, Cancer Res. 2012, 72:645-654; and L. H. Cazares, D. Troyer, S. Mendrinos, R. A. Lance, J. O. Nyalwidhe, H. A. Beydoun, M. A. Clements, R. R. Drake, and O. J. Semmes, Clin. Cancer Res. 2009, 15:5541-5551). IMS makes it possible to monitor many hundreds of biomolecules simultaneously, making it a prime technology for exploratory studies. However, this exploratory advantage is hampered by the large amount of data that a single IMS experiment can deliver, making interpretation and analysis difficult.
The state of the art uses of such information in IMS studies is largely restricted to manual comparison (K. J. Boggio, E. Obasuyi, K. Sugino, S. B. Nelson, N. R. Agar, and J. N. Agar, Expert Rev. Proteomics 2011, 8:591-604; S. N. Whitehead, K. H. N. Chan, S. Gangaraju, J. Slinn, J. Li, and S. T. Hou, PLoS One 2011, 6, e20808; and T. Alexandrov, and J. H. Kobarg, Bioinformatics 2011, 27:i230-8), which poses a practical challenge for larger multi-experiment studies and brings with it a risk of introducing human bias into the analysis. Both supervised and unsupervised computational methods (E. A. Jones, S.-O. Deininger, P. C. W. Hogendoorn, A. M. Deelder, and L. A. McDonnell, J. Proteomics 2012, 75:4962-4989; J. Bruand, T. Alexandrov, S. Sistla, M. Wisztorski, C. Meriaux, M. Becker, M. Salzet, I. Fournier, E. Macagno, and V. Bafna, J. Proteome Res. 2011, 10:4734-4743; and J. M. Fonville, C. L. Carter, L. Pizarro, R. T. Steven, A. D. Palmer, R. L. Griffiths, P. F. Lalor, J. C. Lindon, J. K. Nicholson, E. Holmes, and J. Bunch, Anal. Chem. 2013, 85:1415-1423; and T. Alexandrov, BMC Bioinformatics 2012, 13 Suppl. 1, S11) such as hierarchical clustering, principal component analysis (R. Van de Plas, F. Ojeda, M. Dewil, L. Van Den Bosch, B. De Moor, and E. Waelkens, in Proceedings of the Pacific Symposium on Biocomputing (PSB), Maui, Hi., 2007; pp. 458-469; and G. McCombie, D. Staab, M. Stoeckli, and R. Knochenmuss, Anal. Chem. 2005, 77:6118-6124) and probabilistic latent semantic analysis (M. Hanselmann, M. Kirchner, B. Y. Renard, E. R. Amstalden, K. Glunde, R. M. A. Heeren, and F. A. Hamprecht, Anal. Chem. 2008, 80:9649-9658) have been employed to perform comprehensive analysis of these large data sets. While these methods aid human interpretation by reducing the data size and complexity, they often operate in a blind fashion in the sense that they lack access to an important source of information that many human interpretations rely upon anatomical information on the tissue in question. This information is available in textbooks and through publically accessible anatomical atlases for various organ types and organisms. Mass spectrometry imaging data sets of mouse brains have been mapped to such as the Allen Mouse Brain Reference Atlas (W. M. Abdelmoula, R. J. Carreira, R. Shyti, B. Balluff, R. J. M. van Zeijl, E. A. Tolner, B. F. P. Lelieveldt, A. M. J. M. van den Maagdenberg, L. A. McDonnell, and J. Dijkstra, Anal. Chem. 2014, 86:3947-54).
In order to fully utilize this body of anatomical insight for the interpretation of ion distributions in IMS data, a further computer-traversable bridge between IMS and curated anatomical information is important.
There is a clear need in the art for cross-informing technologies and sources of information, as well as for predictive imaging modalities. This disclosure creates such predictive imaging modality by combining two distinct technologies or image data sources: imaging mass spectrometry (IMS) and anatomical annotations made on microscopy or other imaging tools. Furthermore, once automated spatial registration of IMS data to anatomical information through microscopy is carried out, an algorithmic link between curated anatomical data and empirically acquired IMS data is carried to consequently move beyond registration by applying the established IMS-anatomical atlas link toward automated anatomical interpretation of the ion images obtained through IMS.